After a (purposefully) vague tweet last week, Re/code reached out to get my take on bots, for an overview post they were writing. The quote they used is as follows:

“To be honest, I’m a little worried about the bot hype overtaking the bot reality,” said M.G. Siegler, a partner with GV, the investment firm formerly known as Google Ventures. “Yes, the high level promise of what bots can offer is great. But this isn’t going to happen overnight. And it’s going to take a lot of experimentation and likely bot failure before we get there.”

I suppose this paints me as a bit of a “bot bear” in an age of “bot bulls,” leading up to Facebook’s f8 conference keynote tomorrow, where the company is expected to unveil some sort of new bot platform/tools. So I thought it was worth elaborating a bit — I’ve been thinking about this for a while. Below are my full thoughts I sent over to Re/code:

Over the past year, the Google News Lab expanded into Asia with a focus on fueling innovation in newsrooms. In that time, we’ve seen how chat apps are quickly becoming the preferred medium for digital communication across the region. According to the 2017 Reuters Institute Digital News Report, 23 percent of survey respondents now find, share or discuss news using a chat app—and Asia is at the forefront of this trend.

If I had a penny for every piece of technology fleetingly considered the “future of journalism,” then I suppose I’d have quite a lot of pennies by now, if not quite enough to retire on. Chatbots are one such technology, with CNN, the Wall Street Journal and the Guardian among those launching experimental versions within Facebook Messenger….

I have been extremely lucky to get a chance on designing a chatbot for one of our clients and the learning in the process has been massive. Most of the notions that I thought were true were discarded by research and a whole new world of possibilities just opened wide. Below, I have shared some of my crucial understandings along the path of designing a bot, hope you like it. Continue reading “Designing a Chatbot”→

Remember chatbots? That thing everyone talked about before blockchain swooped in and stole the show? Well, according to Wired, Chatbots are dead. Brands already cooled on chatbots in 2017 and the shutdown of M, a Facebook Messenger bot which automatically completes tasks for users, might be the final nail in the coffin. But should we really be dismissing chatbots that quickly?

We caught up with John Keefe, bot developer at Quartz; Eduardo Suárezand Miguel Eduardo Gil Biraud from Politibot, a chatbot platform that was created in 2016 for the Spanish elections; and Philipp Naderer from Austrian public broadcaster ORF who created the Wahl-bot for the Austrian presidential elections — all to get a better idea of where chatbots stand in early 2018, whether newsrooms should invest in them, and how to make a chatbot live up to our expectations.

In 2017, my team powered chatbots and voice skills for leading brands like Nike, Vice, Jameson, Marriott Rewards, Simon, Gatorade, and more. We witnessed new user behaviors and uncovered an evolved set of best practices to build a chatbot. Here are four actionable learnings from our work that you should consider when launching your own chatbot in 2018.

1. Personalization drives engagement

Bots that are designed to segment and engage customers throughout the entire conversation drive higher metrics than chatbots that do not personalize the conversation. For example, in our testing, personalized results yielded the highest click-through to website, up to 74 percent in some cases.

This year, a leading athletic brand set out to inspire a sneaker style for girls across the globe. The brand launched a customized sneaker builder where the user uploads of a photo of her outfit, and magically, in an instant, the bot pulls up a pair of shoes that matches the uploaded picture. This experience drove a click-through rate 12.5X higher than the global brand average.

Bud Light launched a chatbot with the goal of driving demand and purchase of Bud Light’s team cans on game day throughout the NFL season. A personalized data model and chatbot powered the ordering and delivery of team cans every game day during the NFL season. The Bud Light chatbot acted as a utility to remind fans that it was game time, and to order Bud Light before the game. Bud Light saw an 83 percent engagement rate with personalization.

2. Get to the point quickly

Across multiple chatbots, about half of the first actions that users take is free text entry. Updating the onboarding copy to manage expectations — “this is a bot that can do X and Y,” for example — lowers that initial friction. If the first intent is help-related or a long-form text entry, you can provide a customer service number, FAQs, or an option to “talk to a human” from the very beginning.

When users get into the designed experience, point of sale should be within five clicks. For example, after A/B testing a chatbot across 250,000 users, we noticed a significant drop-off occured when the core focus (click to purchase, etc.) was beyond five clicks.

3. Chatbots go beyond mobile devices

Bots are an effective tool to drive real-world activities or offline conversions, with coupon redemption rates as high as 30 percent.

A leading quick-service restaurant brand launched a new bot that drove users through an immersive content experience with videos, quizzes, recipes, and coupons. This high engagement led to over 71,000 coupons redeemed from the chatbot.

The Jordan Brand aimed to reach elite high school football, basketball, and baseball athletes with an ongoing training chatbot experience for pre-season training. Jordan delivered nightly prep videos and daily workout series to a targeted group of high school athletes in advance of basketball season on Facebook Messenger. Athletes loved receiving push notifications reminding them to work out. Jordan saw an extremely high completion rate as well as a high re-engagement rate compared to regular customer relationship management programs: Over 70 percent of users surveyed enjoyed the experience.

4. Truly understand your users

Understanding why people did or did not enjoy the experience is key. One way to do this is using free text analysis to understand sentiment and drop-off. For example, we launched a new bot with a leading shoe retailer. Most people came to the bot knowing what specific shoe they wanted to buy or with a question about the shoe they already bought. Cater to the specific pain points and make sure your bot handles customer intent at every stage.

Finally, make sure to survey users and learn from both your best purchasers as well as your qualified no’s. One way to do this by asking your users directly. You can use a chatbot for net promoter score surveying.

Jonathan Shriftman is the director of business development at Snaps, a mobile messaging service.

As marketers look into 2018, they see the conversational AI landscape is primed for increased consumer adoption. In fact, in a recent survey, nine out of ten people said they prefer messaging directly with a brand. This year, Apple, Facebook, Google, and Amazon all lean-in to messaging and conversation. In 2018, the big four will make conversational AI the main gateway to communicate with the customer.

Chatbots are an increasing part of our daily lives, redefining how we engage with the internet and with businesses. Canadian messaging company Kik explains it like this: “First there were websites, then there were apps. Now, there are bots.” Just like the early internet, bots are set to transform commerce as we know it, making it easier than ever for consumers to reach, engage, and transact through instant commands. Continue reading “5 tips to humanize your chatbot”→

Maybe, but it’s a lot of work. Here’s what Annenberg Media tried.

Many news organization use chatbots to deliver the news, but few use it to have a conversation. The options for what a user can do beyond asking for the organizations’ top stories tends to be fairly limited.